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Crowd Counting

Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.

Source: Deep Density-aware Count Regressor

Papers

Showing 201225 of 371 papers

TitleStatusHype
Direct Measure Matching for Crowd Counting0
NAS-Count: Counting-by-Density with Neural Architecture Search0
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images0
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning0
PaDNet: Pan-Density Crowd Counting0
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting0
Parametric Regression on the Grassmannian0
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting0
People Counting in High Density Crowds from Still Images0
Pixel-wise Crowd Understanding via Synthetic Data0
Plug-and-Play Rescaling Based Crowd Counting in Static Images0
Point in, Box out: Beyond Counting Persons in Crowds0
POPCat: Propagation of particles for complex annotation tasks0
ProgRoCC: A Progressive Approach to Rough Crowd Counting0
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks0
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method0
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization0
Recurrent Distillation based Crowd Counting0
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting0
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting0
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting0
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting0
Relational Attention Network for Crowd Counting0
Relevant Region Prediction for Crowd Counting0
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives0
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